Cognitive digital modulation classifier using artificial neural networks for NGNs

Author

Abdel Hafiz El Rube, Ibrahim ; El-Madany, Nour El-Din

Author_Institution

Dept. of the Electron. & Commun. Eng., Arab Acad. for Sci. & Technol., Alexandria, Egypt

fYear

2010

fDate

6-8 Sept. 2010

Firstpage

1

Lastpage

5

Abstract

An automatic cognitive classification scheme for modulation is developed and presented in this paper. With the help of this scheme, the classification and recognition cognitively of wireless communication signals is possible. The proposed features of the scheme have the possibility to adapt it dynamically to any modulation technique, and the capability to identify it correctly. The developed scheme is based on temporal or statistical parameters have been used to identify M-ary PSK, M-ary QAM, and FSK modulations. The simulated results show that the modulation classification is possible at very low SNR. Experimental results lead us to a recognition percentage of 99%, when SNR is 1 dB.

Keywords

modulation; neural nets; pattern classification; radiocommunication; FSK modulation; M-ary PSK modulation; M-ary QAM modulation; NGN; artificial neural networks; automatic cognitive classification; cognitive digital modulation classifier; wireless communication signals; Artificial neural networks; Classification algorithms; Digital modulation; Feature extraction; Phase shift keying; Quadrature amplitude modulation; Automatic Modulation Classification; Multilayer preceptron; Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Wireless And Optical Communications Networks (WOCN), 2010 Seventh International Conference On

Conference_Location

Colombo

Print_ISBN

978-1-4244-7203-1

Type

conf

DOI

10.1109/WOCN.2010.5587330

Filename

5587330